Consistency-Based Losses: There are several inherent constraints in the motion forecasting problem that have been discussed in the literature. [1]} introduced temporal and spatial consistency losses. Temporal consistency enforces that inputs shifted by a small time interval produce similar output trajectories. Spatial consistency enforces that inputs perturbed by a small amount of noise produce similar output trajectories. The loss introduced by [2]} is the closest to our work in motion prediction of autonomous vehicles. They pass learning embeddings for the future timesteps through a temporal flow network to reconstruct the input. However, since they use a separate model for reconstruction, it is not truly a consistency loss; but rather a method to enrich the learned embeddings. In our work, we use the same model to predict forward and backward in time, making our method a consistency loss on the model. Another difference between our cycle loss method and [2]} is that we use the predicted trajectories to do backward prediction, while [2]} uses feature embeddings. [5]} uses a similar approach to ours for human trajectory prediction. They pretrain two models for forward and backward prediction and refine the predictions for both models by jointly training both models with cycle loss. This makes their training procedure lengthy as they have to train three times. Our approach differs since we use the same model for forward and backward prediction making our training end-to-end and therefore much faster than [5]}. This also makes cycle loss a true consistency loss on the model which is not the case for [5]}. The element of ground truth trajectory mixing is crucial for our method to work.
